{"title":"一个用Transformer生成期刊文章标题的方法","authors":"Matsumoto Riku, Kimura Masaomi","doi":"10.23919/APSIPAASC55919.2022.9979942","DOIUrl":null,"url":null,"abstract":"While many methods of summarization have been proposed, there have been few methods to generate a title, especially for journal articles. However, the differences between summarization and creating a title are length and clause form. We propose a title generation model for a journal article based on Transformer, which refers to a wide range of the article. We propose to narrow down the abstract sentences to only important sentences before title generation so that the author's claim can be easily reflected in the title. We applied our method to journal articles published on arXiv.org and found that our model generated a title including words in the original title.","PeriodicalId":382967,"journal":{"name":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A title generation method with Transformer for journal articles\",\"authors\":\"Matsumoto Riku, Kimura Masaomi\",\"doi\":\"10.23919/APSIPAASC55919.2022.9979942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While many methods of summarization have been proposed, there have been few methods to generate a title, especially for journal articles. However, the differences between summarization and creating a title are length and clause form. We propose a title generation model for a journal article based on Transformer, which refers to a wide range of the article. We propose to narrow down the abstract sentences to only important sentences before title generation so that the author's claim can be easily reflected in the title. We applied our method to journal articles published on arXiv.org and found that our model generated a title including words in the original title.\",\"PeriodicalId\":382967,\"journal\":{\"name\":\"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)\",\"volume\":\"262 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/APSIPAASC55919.2022.9979942\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/APSIPAASC55919.2022.9979942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A title generation method with Transformer for journal articles
While many methods of summarization have been proposed, there have been few methods to generate a title, especially for journal articles. However, the differences between summarization and creating a title are length and clause form. We propose a title generation model for a journal article based on Transformer, which refers to a wide range of the article. We propose to narrow down the abstract sentences to only important sentences before title generation so that the author's claim can be easily reflected in the title. We applied our method to journal articles published on arXiv.org and found that our model generated a title including words in the original title.